Research

Full Stack Robotics

Collective Perception and Decision Making in a Robot Swarm

Advisor: Prof. Radhika Nagpal, Harvard University

This research aims to improve the ability of a large group of robots to perceive and classify their environment by employing robots with different perceptual skills. The ability to make collective decisions is a critical component to developing complex collective behavior and intelligence and can contribute to the broader challenge of translating global goals to local rules.

In a first paper, we demonstrated that a bio-inspired algorithm that allowed a collective of Kilobots to discriminate between multiple binary-state features simultaneously. We also explored strategies for allocating robots between features, finding approaches that proved successful even when the initial distribution of robots across features was poor.

Currently, I am developing a more general framework for distributed Bayesian decision-making in robots.

LARVAbots: Locomotion of Autonomous Robots Via Aggregation

Advisor: Prof. Radhika Nagpal, Harvard University

Sawfly larva move together in a large aggregate, possibly giving them energetic advantages for reduced movement effort, exploiting the sensing of a few individuals, avoiding losing members of the collective, or overcoming obstacles.

I am desiging and building a group of larva-inspired robots capable of similar collective movement. These LARVAbots can maintain an aggregate as they move and overcome obstacles by exploiting the shape of the group. Currently, I am investigating whether their collective behavior can result in greater movement efficiency than the movement of individual robots.

Collaborative Autonomy for Space Situational Awareness

Tracking satellites is an important component of space situational awareness (SSA). However, current ground-based tracking approaches rely on centralized detection and require hours to accurately estimate an orbit. A constellation of low-cost, autonomous cube satellites could provide a fast and robustly decentralized architecture for SSA. We propose distributed particles filters as a method to iteratively refine orbit estimates with low communication bandwidth. We demonstrate the feasibility of this approach by implementing our algorithm in simulation. This simulator can also be used to evaluate the parameter space for future satellite constellation design, as well as test the system's robustness to failures.

Cooperative Exoskeleton Control for Human Balance Recovery

Maintaining balance in the face of perpurbations is essential to walking and standing. For my masters thesis, I developed controls for LOPES (LOwer-extremity Powered ExoSkeleton, University of Twente) to assist humans with balance recovery after perturbations, using a combination of feed-forward and feedback control (such as hip torques and a PD controller). We found that even simple, single-joint torques are sufficient to reduce the time to a recovery movement and the energy used by subjects in recovery.

Stability and Predictability in Human Control of Complex Objects

Advisor: Prof. Dagmar Sternad, Northeastern University

We examined human control of physical interaction with objects that exhibit complex dynamics, hypothesizing that humans exploited stability properties of the human-object interaction. Using a simplified 2D model for carrying a cup of coffee, we developed a virtual implementation to identify human control strategies. The specific task was to transport the cart-pendulum model of a cup of coffee to a target, as fast as possible, while accommodating assistive and resistive perturbations. To assess trajectory stability, we applied contraction analysis. We showed that when the perturbation was assistive, humans absorbed the perturbation by controlling cart trajectories into a contraction region prior to the perturbation. When the perturbation was resistive, subjects passed through a contraction region following the perturbation. Entering a contraction region stabilizes performance and makes the dynamics more predictable. This human control strategy could inspire more robust control strategies for physical interaction in robots.

Bimanual Learning and Retention

Advisor: Prof. Dagmar Sternad, Northeastern University

Participants were asked to perform a task in which one arm is moved rhythmically while the other makes fast, discrete movements when cued.
Over 20 sessions of practice, participants learned the task asymmetrically: while they learned to make much faster discrete movements, they failed to attenuate the perturbation these discrete movements caused in the rhythmic arm. After 6 months, subjects retained the skills they learned, including the asymmetry.

I proposed this research in my application the Barry Goldwater Scholarship and conducted the work for my undergraduate honors thesis. It was also funded by two Provost Undergraduate Research Awards.